Nadin is a scholar and researcher in electrical engineering, computer science, aesthetics, semiotics, human-computer interaction (HCI), computational design, post-industrial society, and anticipatory systems. His publications on these topics number over 200, and he has lectured throughout the world.

Since his first involvement with the computer in the 1960’s, Nadin has espoused ways to involve computing in education and creativity, and later, with the advent of desktop computers, in art and design education. He taught some of the first known classes in many areas related to computer science.

Interview Highlights

On Art and Aesthetics

There is no such thing as “digital art” just as there is no “digital humanities”. Something qualifies as art (or humanities) or it does not. All the formulations that have emerged parallel to increased computer use are an attempt to signal keeping pace with science and technology; but fundamentally they do nothing to define new forms of writing and composing, new types of performance.

On Media Literacy

In reality, “Humanities” is the expression of resistance. Those involved in humanities probe the science and technology instead of automatically accepting them.

On Big Data

Data becomes information only when it is associated with meaning. However, our age is one of unreflected data generation, not one of quest for meaning. Data production (“Give me the numbers!”) is the new religion. Politics, economics, and science are all reduced to data production. Ownership of data replaced ownership of land, tools, and machines. Human interaction is also reduced to data production: what we buy, where we buy, whom we talk to, for how long, how often, etc. The Internet as the conduit for data is boring and deceiving. This is not what Vinton Cerf, to whose name the global transmission protocol TCP/IP is attached, had in mind.

On Algorithmic Computation as a Research Tool

It turns out that quite a number of problems – the most interesting ones, actually – are not algorithmic. Protein folding, essential in living processes, is one example. So is computer graphics, involving interactive elements. Furthermore, adaptive processes can not be described through algorithmic rules. More important, anticipatory processes refuse to fit into neat algorithmic schemes.

At the time when I advanced the notion that the computer is a semiotic engine, my enthusiasm was way ahead of my ability to understand that the so-called universal machine is actually one of many others. Today we know of DNA programming, neural network computation, and membrane computation, some equivalent to a Turing machine, some not.